Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations10336
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory152.0 B

Variable types

Numeric19

Alerts

agrprod is highly overall correlated with harvest and 1 other fieldsHigh correlation
avgemployers is highly overall correlated with beforeschool and 9 other fieldsHigh correlation
avgsalary is highly overall correlated with factoriescap and 1 other fieldsHigh correlation
beforeschool is highly overall correlated with avgemployers and 9 other fieldsHigh correlation
consnewareas is highly overall correlated with avgemployers and 10 other fieldsHigh correlation
factoriescap is highly overall correlated with avgemployers and 10 other fieldsHigh correlation
foodseats is highly overall correlated with avgemployers and 9 other fieldsHigh correlation
funds is highly overall correlated with avgemployers and 9 other fieldsHigh correlation
harvest is highly overall correlated with agrprod and 3 other fieldsHigh correlation
hospitals is highly overall correlated with sportsvenueHigh correlation
livestock is highly overall correlated with agrprodHigh correlation
popsize is highly overall correlated with avgemployers and 10 other fieldsHigh correlation
retailturnover is highly overall correlated with avgemployers and 10 other fieldsHigh correlation
servicesnum is highly overall correlated with avgemployers and 9 other fieldsHigh correlation
shoparea is highly overall correlated with avgemployers and 9 other fieldsHigh correlation
sportsvenue is highly overall correlated with avgemployers and 11 other fieldsHigh correlation
livarea is highly skewed (γ1 = 54.15315213) Skewed
livestock is highly skewed (γ1 = 56.88941897) Skewed
harvest is highly skewed (γ1 = 33.84708554) Skewed

Reproduction

Analysis started2024-10-20 11:38:12.466538
Analysis finished2024-10-20 11:38:30.344811
Duration17.88 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

saldo
Real number (ℝ)

Distinct2105
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0018106226
Minimum-0.20607266
Maximum1
Zeros16
Zeros (%)0.2%
Negative7284
Negative (%)70.5%
Memory size80.9 KiB
2024-10-20T14:38:30.400747image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-0.20607266
5-th percentile-0.013658451
Q1-0.0053878496
median-0.0023666255
Q30.00070495229
95-th percentile0.027625318
Maximum1
Range1.2060727
Interquartile range (IQR)0.0060928019

Descriptive statistics

Standard deviation0.035334775
Coefficient of variation (CV)19.515263
Kurtosis228.09835
Mean0.0018106226
Median Absolute Deviation (MAD)0.003046401
Skewness11.938419
Sum18.714595
Variance0.0012485463
MonotonicityNot monotonic
2024-10-20T14:38:30.473850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.002568040484 34
 
0.3%
-0.001837911327 33
 
0.3%
-0.002492509882 33
 
0.3%
-0.002844986027 32
 
0.3%
-0.002542863617 32
 
0.3%
-0.003977945064 31
 
0.3%
-0.003172285304 31
 
0.3%
-0.001132959037 31
 
0.3%
-0.002391802412 31
 
0.3%
-0.004305244341 31
 
0.3%
Other values (2095) 10017
96.9%
ValueCountFrequency (%)
-0.2060726604 1
< 0.1%
-0.1946675395 1
< 0.1%
-0.1790075279 1
< 0.1%
-0.1622145573 1
< 0.1%
-0.1553916262 1
< 0.1%
-0.1471839674 1
< 0.1%
-0.1436088522 1
< 0.1%
-0.1382713563 1
< 0.1%
-0.1292328608 1
< 0.1%
-0.1236687731 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.903673305 1
< 0.1%
0.8743422543 1
< 0.1%
0.7134117173 1
< 0.1%
0.7029884942 1
< 0.1%
0.6663309751 1
< 0.1%
0.5894659986 1
< 0.1%
0.5219668169 1
< 0.1%
0.4823384275 1
< 0.1%
0.4811299378 1
< 0.1%

popsize
Real number (ℝ)

High correlation 

Distinct9534
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.038002499
Minimum0.002156701
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.9 KiB
2024-10-20T14:38:30.540419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.002156701
5-th percentile0.0056107444
Q10.0099721277
median0.016311512
Q30.030967514
95-th percentile0.12874262
Maximum1
Range0.9978433
Interquartile range (IQR)0.020995386

Descriptive statistics

Standard deviation0.083633913
Coefficient of variation (CV)2.2007477
Kurtosis49.025699
Mean0.038002499
Median Absolute Deviation (MAD)0.0080630229
Skewness6.3700961
Sum392.79383
Variance0.0069946314
MonotonicityNot monotonic
2024-10-20T14:38:30.607563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.008760290619 5
 
< 0.1%
0.006958528719 4
 
< 0.1%
0.007258104699 4
 
< 0.1%
0.01055159504 4
 
< 0.1%
0.007467254254 3
 
< 0.1%
0.01346677075 3
 
< 0.1%
0.00687732948 3
 
< 0.1%
0.01070845721 3
 
< 0.1%
0.006689094881 3
 
< 0.1%
0.02570325 3
 
< 0.1%
Other values (9524) 10301
99.7%
ValueCountFrequency (%)
0.002156700998 1
< 0.1%
0.00221575499 1
< 0.1%
0.002272963545 1
< 0.1%
0.002357238512 1
< 0.1%
0.002382459488 1
< 0.1%
0.00239230182 1
< 0.1%
0.002419983379 1
< 0.1%
0.002434131731 1
< 0.1%
0.002452586103 1
< 0.1%
0.002490109994 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9953298135 1
< 0.1%
0.9921273647 1
< 0.1%
0.9860263492 1
< 0.1%
0.9744757574 1
< 0.1%
0.9639869072 1
< 0.1%
0.9521902572 1
< 0.1%
0.9396505111 1
< 0.1%
0.9389486298 1
< 0.1%
0.9324576118 1
< 0.1%

avgemployers
Real number (ℝ)

High correlation 

Distinct7308
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.031093723
Minimum0.00097913818
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.9 KiB
2024-10-20T14:38:30.672668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.00097913818
5-th percentile0.0028514092
Q10.0055660918
median0.010273232
Q30.022213095
95-th percentile0.11433478
Maximum1
Range0.99902086
Interquartile range (IQR)0.016647003

Descriptive statistics

Standard deviation0.083177943
Coefficient of variation (CV)2.6750719
Kurtosis54.373109
Mean0.031093723
Median Absolute Deviation (MAD)0.0058075685
Skewness6.717698
Sum321.38472
Variance0.0069185702
MonotonicityNot monotonic
2024-10-20T14:38:30.741186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.004397300754 8
 
0.1%
0.00447448507 8
 
0.1%
0.006163718961 6
 
0.1%
0.004527411459 6
 
0.1%
0.00573148679 6
 
0.1%
0.007469236537 6
 
0.1%
0.002851409165 6
 
0.1%
0.003978300181 6
 
0.1%
0.005698407798 6
 
0.1%
0.003329951925 6
 
0.1%
Other values (7298) 10272
99.4%
ValueCountFrequency (%)
0.000979138182 1
< 0.1%
0.0009813434482 1
< 0.1%
0.0009945750452 1
< 0.1%
0.001069554095 1
< 0.1%
0.001073964628 1
< 0.1%
0.001082785692 1
< 0.1%
0.001109248886 1
< 0.1%
0.001111454153 1
< 0.1%
0.001113659419 1
< 0.1%
0.001122480483 2
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9973051647 1
< 0.1%
0.9874454197 1
< 0.1%
0.987050677 1
< 0.1%
0.9820866229 1
< 0.1%
0.9757442773 1
< 0.1%
0.9748224761 1
< 0.1%
0.9726150046 1
< 0.1%
0.9536519208 1
< 0.1%
0.9505689587 1
< 0.1%

avgsalary
Real number (ℝ)

High correlation 

Distinct10193
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.063628951
Minimum0.023724411
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.9 KiB
2024-10-20T14:38:30.807214image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.023724411
5-th percentile0.035787215
Q10.045613678
median0.055640642
Q30.069443545
95-th percentile0.11194763
Maximum1
Range0.97627559
Interquartile range (IQR)0.023829867

Descriptive statistics

Standard deviation0.042876101
Coefficient of variation (CV)0.67384579
Kurtosis153.2604
Mean0.063628951
Median Absolute Deviation (MAD)0.011490025
Skewness10.136231
Sum657.66884
Variance0.00183836
MonotonicityNot monotonic
2024-10-20T14:38:30.871409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0401810864 3
 
< 0.1%
0.05097126186 2
 
< 0.1%
0.05641668427 2
 
< 0.1%
0.03847997173 2
 
< 0.1%
0.03606173597 2
 
< 0.1%
0.06440765956 2
 
< 0.1%
0.0588159705 2
 
< 0.1%
0.03833212596 2
 
< 0.1%
0.05248031519 2
 
< 0.1%
0.04670288131 2
 
< 0.1%
Other values (10183) 10315
99.8%
ValueCountFrequency (%)
0.02372441074 1
< 0.1%
0.02566555272 1
< 0.1%
0.0267470574 1
< 0.1%
0.02720204339 1
< 0.1%
0.02810806756 1
< 0.1%
0.02811063364 1
< 0.1%
0.02874721926 1
< 0.1%
0.02882716257 1
< 0.1%
0.02911101066 1
< 0.1%
0.02914061929 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9375356537 1
< 0.1%
0.9060972071 1
< 0.1%
0.8933398341 1
< 0.1%
0.8483208944 1
< 0.1%
0.8269553048 1
< 0.1%
0.763028292 1
< 0.1%
0.7158893743 1
< 0.1%
0.6681306491 1
< 0.1%
0.6509043463 1
< 0.1%

shoparea
Real number (ℝ)

High correlation 

Distinct9746
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.021842466
Minimum0.00036467989
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.9 KiB
2024-10-20T14:38:30.935142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.00036467989
5-th percentile0.0017099987
Q10.0032458922
median0.0057977754
Q30.013263438
95-th percentile0.083807405
Maximum1
Range0.99963532
Interquartile range (IQR)0.010017546

Descriptive statistics

Standard deviation0.066314429
Coefficient of variation (CV)3.0360321
Kurtosis65.231402
Mean0.021842466
Median Absolute Deviation (MAD)0.0032980114
Skewness7.234666
Sum225.76373
Variance0.0043976035
MonotonicityNot monotonic
2024-10-20T14:38:31.000365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04650245765 7
 
0.1%
0.004349815661 7
 
0.1%
0.01053564575 7
 
0.1%
0.001998648631 6
 
0.1%
0.005025376295 5
 
< 0.1%
0.002656234991 5
 
< 0.1%
0.0027034793 5
 
< 0.1%
0.007620515243 5
 
< 0.1%
0.002732831785 4
 
< 0.1%
0.009567178645 4
 
< 0.1%
Other values (9736) 10281
99.5%
ValueCountFrequency (%)
0.000364679891 1
< 0.1%
0.0004088735201 2
< 0.1%
0.0004303519536 1
< 0.1%
0.0004926435334 1
< 0.1%
0.0004951582829 1
< 0.1%
0.000507938157 1
< 0.1%
0.0005330856511 1
< 0.1%
0.0005367959372 1
< 0.1%
0.0005369608388 1
< 0.1%
0.0005561306499 2
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.968890035 1
< 0.1%
0.9459370101 1
< 0.1%
0.9456731675 1
< 0.1%
0.9391405906 1
< 0.1%
0.9261972578 1
< 0.1%
0.8925913421 1
< 0.1%
0.8333520084 1
< 0.1%
0.8293313776 1
< 0.1%
0.7942897874 1
< 0.1%

foodseats
Real number (ℝ)

High correlation 

Distinct2989
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.027896343
Minimum0
Maximum1
Zeros21
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size80.9 KiB
2024-10-20T14:38:31.063897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0011362022
Q10.0036642522
median0.0079392132
Q30.017824173
95-th percentile0.11032524
Maximum1
Range1
Interquartile range (IQR)0.01415992

Descriptive statistics

Standard deviation0.080231856
Coefficient of variation (CV)2.8760708
Kurtosis54.35801
Mean0.027896343
Median Absolute Deviation (MAD)0.0053117455
Skewness6.7321915
Sum288.3366
Variance0.0064371507
MonotonicityNot monotonic
2024-10-20T14:38:31.127466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.001136202244 51
 
0.5%
0.001704303366 45
 
0.4%
0.000568101122 45
 
0.4%
0.000852151683 39
 
0.4%
0.003834682573 39
 
0.4%
0.003550632012 38
 
0.4%
0.0009941769635 37
 
0.4%
0.003408606732 36
 
0.3%
0.00284050561 36
 
0.3%
0.001562278085 35
 
0.3%
Other values (2979) 9935
96.1%
ValueCountFrequency (%)
0 21
0.2%
0.0001136202244 2
 
< 0.1%
0.0001420252805 5
 
< 0.1%
0.0002272404488 2
 
< 0.1%
0.0002414429768 1
 
< 0.1%
0.0002698480329 1
 
< 0.1%
0.000284050561 8
 
0.1%
0.0003408606732 12
0.1%
0.0003550632012 3
 
< 0.1%
0.0003692657293 5
 
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9991194433 1
< 0.1%
0.9362590541 1
< 0.1%
0.9245277659 1
< 0.1%
0.8989206079 1
< 0.1%
0.8977560006 1
< 0.1%
0.8967902287 1
< 0.1%
0.8921602045 1
< 0.1%
0.8865218009 1
< 0.1%
0.8848174975 1
< 0.1%

retailturnover
Real number (ℝ)

High correlation 

Distinct10334
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.012322769
Minimum6.9419753 × 10-8
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.9 KiB
2024-10-20T14:38:31.191104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6.9419753 × 10-8
5-th percentile0.00034483819
Q10.00092394594
median0.0019521319
Q30.0053310939
95-th percentile0.044617417
Maximum1
Range0.99999993
Interquartile range (IQR)0.0044071479

Descriptive statistics

Standard deviation0.048750562
Coefficient of variation (CV)3.9561368
Kurtosis103.3532
Mean0.012322769
Median Absolute Deviation (MAD)0.001310705
Skewness8.9316348
Sum127.36814
Variance0.0023766173
MonotonicityNot monotonic
2024-10-20T14:38:31.256916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.001911794296 2
 
< 0.1%
0.001462893518 2
 
< 0.1%
0.001468562027 1
 
< 0.1%
0.02005398092 1
 
< 0.1%
0.006626755919 1
 
< 0.1%
0.001510084552 1
 
< 0.1%
0.001854768797 1
 
< 0.1%
0.002204747969 1
 
< 0.1%
0.002670411047 1
 
< 0.1%
0.003121409844 1
 
< 0.1%
Other values (10324) 10324
99.9%
ValueCountFrequency (%)
6.941975335 × 10-81
< 0.1%
2.401409245 × 10-71
< 0.1%
4.170327405 × 10-71
< 0.1%
1.711582585 × 10-61
< 0.1%
2.52482214 × 10-61
< 0.1%
2.891975502 × 10-61
< 0.1%
2.956767272 × 10-61
< 0.1%
3.437563342 × 10-61
< 0.1%
3.595171893 × 10-61
< 0.1%
3.653535908 × 10-61
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9168060748 1
< 0.1%
0.8178439682 1
< 0.1%
0.7763987359 1
< 0.1%
0.7226929932 1
< 0.1%
0.7132970211 1
< 0.1%
0.7015944239 1
< 0.1%
0.6913388563 1
< 0.1%
0.6486048928 1
< 0.1%
0.6314123437 1
< 0.1%

consnewareas
Real number (ℝ)

High correlation 

Distinct8550
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.013650766
Minimum0
Maximum1
Zeros7
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size80.9 KiB
2024-10-20T14:38:31.320949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00025381816
Q10.0010863456
median0.0027034452
Q30.006999535
95-th percentile0.055363597
Maximum1
Range1
Interquartile range (IQR)0.0059131894

Descriptive statistics

Standard deviation0.046501128
Coefficient of variation (CV)3.4064849
Kurtosis114.39677
Mean0.013650766
Median Absolute Deviation (MAD)0.0020169803
Skewness9.0351171
Sum141.09432
Variance0.0021623549
MonotonicityNot monotonic
2024-10-20T14:38:31.385373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0006878023226 8
 
0.1%
0 7
 
0.1%
0.0004967461218 6
 
0.1%
0.001092077243 6
 
0.1%
0.001413815885 6
 
0.1%
0.000798614919 6
 
0.1%
0.0003056899211 5
 
< 0.1%
0.0007714849385 5
 
< 0.1%
0.0003966326727 5
 
< 0.1%
0.000976679298 5
 
< 0.1%
Other values (8540) 10277
99.4%
ValueCountFrequency (%)
0 7
0.1%
5.731686021 × 10-61
 
< 0.1%
6.113798423 × 10-61
 
< 0.1%
7.260135627 × 10-61
 
< 0.1%
8.406472831 × 10-61
 
< 0.1%
9.552810035 × 10-61
 
< 0.1%
1.146337204 × 10-51
 
< 0.1%
1.184548444 × 10-51
 
< 0.1%
1.260970925 × 10-51
 
< 0.1%
1.413815885 × 10-52
 
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9365926502 1
< 0.1%
0.8131741657 1
< 0.1%
0.7721513425 1
< 0.1%
0.7678403504 1
< 0.1%
0.7206166071 1
< 0.1%
0.7144649796 1
< 0.1%
0.7069132922 1
< 0.1%
0.7034521181 1
< 0.1%
0.6641002724 1
< 0.1%

livarea
Real number (ℝ)

Skewed 

Distinct1825
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0026481106
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size80.9 KiB
2024-10-20T14:38:31.450418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0016424682
Q10.0019263122
median0.0021527495
Q30.0024876217
95-th percentile0.0032040089
Maximum1
Range1
Interquartile range (IQR)0.00056130951

Descriptive statistics

Standard deviation0.015277099
Coefficient of variation (CV)5.7690562
Kurtosis3217.0907
Mean0.0026481106
Median Absolute Deviation (MAD)0.00026391115
Skewness54.153152
Sum27.370871
Variance0.00023338975
MonotonicityNot monotonic
2024-10-20T14:38:31.516454image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.001993286611 85
 
0.8%
0.002088964368 76
 
0.7%
0.00201720605 75
 
0.7%
0.002025179196 75
 
0.7%
0.002009232904 73
 
0.7%
0.002033152343 73
 
0.7%
0.001969367171 72
 
0.7%
0.001945447732 71
 
0.7%
0.002041125489 71
 
0.7%
0.001921528293 70
 
0.7%
Other values (1815) 9595
92.8%
ValueCountFrequency (%)
0 1
< 0.1%
0.0002758708669 1
< 0.1%
0.0007893414978 1
< 0.1%
0.0008132609372 2
< 0.1%
0.0008371803765 1
< 0.1%
0.0008850192551 1
< 0.1%
0.0008906004577 1
< 0.1%
0.0008929924016 1
< 0.1%
0.000986278215 1
< 0.1%
0.001028535891 2
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9287519634 1
< 0.1%
0.4594206712 1
< 0.1%
0.4421428628 1
< 0.1%
0.2228574162 1
< 0.1%
0.2178423071 1
< 0.1%
0.1892984428 1
< 0.1%
0.1054209423 1
< 0.1%
0.04791861012 1
< 0.1%
0.02910198452 2
< 0.1%

sportsvenue
Real number (ℝ)

High correlation 

Distinct736
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.031568714
Minimum0.0004730369
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.9 KiB
2024-10-20T14:38:31.580134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.0004730369
5-th percentile0.0059129612
Q10.012062441
median0.018921476
Q30.030983917
95-th percentile0.082781457
Maximum1
Range0.99952696
Interquartile range (IQR)0.018921476

Descriptive statistics

Standard deviation0.053664099
Coefficient of variation (CV)1.699914
Kurtosis71.53809
Mean0.031568714
Median Absolute Deviation (MAD)0.0082781457
Skewness7.168372
Sum326.29423
Variance0.0028798355
MonotonicityNot monotonic
2024-10-20T14:38:31.649778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01229895932 118
 
1.1%
0.01537369915 117
 
1.1%
0.0146641438 109
 
1.1%
0.01679280984 106
 
1.0%
0.01371807001 102
 
1.0%
0.01158940397 101
 
1.0%
0.01608325449 98
 
0.9%
0.01253547777 97
 
0.9%
0.01135288553 97
 
0.9%
0.0156102176 96
 
0.9%
Other values (726) 9295
89.9%
ValueCountFrequency (%)
0.0004730368969 1
 
< 0.1%
0.0007095553453 3
 
< 0.1%
0.001182592242 3
 
< 0.1%
0.001419110691 3
 
< 0.1%
0.002128666036 10
 
0.1%
0.002365184484 16
0.2%
0.002601702933 8
 
0.1%
0.002838221381 25
0.2%
0.00307473983 20
0.2%
0.003311258278 21
0.2%
ValueCountFrequency (%)
1 1
< 0.1%
0.9808420057 1
< 0.1%
0.8353831599 1
< 0.1%
0.8209555345 1
< 0.1%
0.8036896878 1
< 0.1%
0.7306054872 1
< 0.1%
0.7180700095 1
< 0.1%
0.6577578051 1
< 0.1%
0.6310312204 1
< 0.1%
0.610217597 1
< 0.1%

servicesnum
Real number (ℝ)

High correlation 

Distinct939
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.023988947
Minimum0.00014997001
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.9 KiB
2024-10-20T14:38:31.718223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.00014997001
5-th percentile0.0019496101
Q10.0044991002
median0.0086982603
Q30.019346131
95-th percentile0.088069886
Maximum1
Range0.99985003
Interquartile range (IQR)0.014847031

Descriptive statistics

Standard deviation0.058695579
Coefficient of variation (CV)2.4467759
Kurtosis75.803687
Mean0.023988947
Median Absolute Deviation (MAD)0.0052489502
Skewness7.3832014
Sum247.94976
Variance0.003445171
MonotonicityNot monotonic
2024-10-20T14:38:31.864123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.003599280144 149
 
1.4%
0.004199160168 138
 
1.3%
0.00374925015 136
 
1.3%
0.004049190162 136
 
1.3%
0.003149370126 134
 
1.3%
0.003449310138 134
 
1.3%
0.002699460108 130
 
1.3%
0.002849430114 128
 
1.2%
0.004649070186 127
 
1.2%
0.004799040192 127
 
1.2%
Other values (929) 8997
87.0%
ValueCountFrequency (%)
0.000149970006 11
 
0.1%
0.000299940012 7
 
0.1%
0.000449910018 6
 
0.1%
0.000599880024 24
 
0.2%
0.00074985003 18
 
0.2%
0.000899820036 26
 
0.3%
0.001049790042 50
0.5%
0.001199760048 77
0.7%
0.001349730054 57
0.6%
0.00149970006 65
0.6%
ValueCountFrequency (%)
1 1
< 0.1%
0.9733053389 1
< 0.1%
0.9610077984 1
< 0.1%
0.9563587283 1
< 0.1%
0.9541091782 1
< 0.1%
0.9388122376 1
< 0.1%
0.8975704859 1
< 0.1%
0.8570785843 1
< 0.1%
0.6783143371 1
< 0.1%
0.6399220156 2
< 0.1%

roadslen
Real number (ℝ)

Distinct4842
Distinct (%)46.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13416623
Minimum0.00033350514
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.9 KiB
2024-10-20T14:38:31.927923image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.00033350514
5-th percentile0.029644059
Q10.070786466
median0.11342207
Q30.17269502
95-th percentile0.30876512
Maximum1
Range0.99966649
Interquartile range (IQR)0.10190856

Descriptive statistics

Standard deviation0.094670543
Coefficient of variation (CV)0.70562124
Kurtosis9.6294256
Mean0.13416623
Median Absolute Deviation (MAD)0.049449717
Skewness2.2452161
Sum1386.7422
Variance0.0089625118
MonotonicityNot monotonic
2024-10-20T14:38:31.994993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1913106752 15
 
0.1%
0.1680865901 15
 
0.1%
0.03444198527 13
 
0.1%
0.1830943213 13
 
0.1%
0.1173331716 12
 
0.1%
0.1136949338 12
 
0.1%
0.06309310857 12
 
0.1%
0.08216353879 12
 
0.1%
0.1767880423 12
 
0.1%
0.1122396386 11
 
0.1%
Other values (4832) 10209
98.8%
ValueCountFrequency (%)
0.000333505139 9
0.1%
0.001152108662 1
 
< 0.1%
0.001667525695 2
 
< 0.1%
0.002607403814 1
 
< 0.1%
0.002668041112 1
 
< 0.1%
0.002819634357 2
 
< 0.1%
0.003092502198 4
< 0.1%
0.003577600582 1
 
< 0.1%
0.004032380317 1
 
< 0.1%
0.004820665191 2
 
< 0.1%
ValueCountFrequency (%)
1 3
< 0.1%
0.9997271322 3
< 0.1%
0.8645665949 1
 
< 0.1%
0.8523481794 1
 
< 0.1%
0.8368856684 1
 
< 0.1%
0.743110087 4
< 0.1%
0.6818057787 2
< 0.1%
0.6783191341 1
 
< 0.1%
0.6673741018 2
< 0.1%
0.6651608404 4
< 0.1%

livestock
Real number (ℝ)

High correlation  Skewed 

Distinct9863
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0013516121
Minimum1.9999985 × 10-8
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.9 KiB
2024-10-20T14:38:32.059937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1.9999985 × 10-8
5-th percentile7.8637442 × 10-6
Q15.9803706 × 10-5
median0.00017632237
Q30.00049097964
95-th percentile0.0049686126
Maximum1
Range0.99999998
Interquartile range (IQR)0.00043117593

Descriptive statistics

Standard deviation0.012905025
Coefficient of variation (CV)9.5478764
Kurtosis3914.9248
Mean0.0013516121
Median Absolute Deviation (MAD)0.00014520989
Skewness56.889419
Sum13.970262
Variance0.00016653967
MonotonicityNot monotonic
2024-10-20T14:38:32.125122image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.129997699 × 10-64
 
< 0.1%
1.844998644 × 10-64
 
< 0.1%
1.664998776 × 10-54
 
< 0.1%
8.324993881 × 10-63
 
< 0.1%
2.92549785 × 10-53
 
< 0.1%
3.854997167 × 10-63
 
< 0.1%
1.084499203 × 10-53
 
< 0.1%
1.125499173 × 10-53
 
< 0.1%
4.049997023 × 10-73
 
< 0.1%
5.869995686 × 10-63
 
< 0.1%
Other values (9853) 10303
99.7%
ValueCountFrequency (%)
1.99999853 × 10-82
< 0.1%
3.499997428 × 10-81
 
< 0.1%
4.499996693 × 10-82
< 0.1%
4.999996325 × 10-82
< 0.1%
6.999994855 × 10-81
 
< 0.1%
7.499994488 × 10-82
< 0.1%
7.99999412 × 10-82
< 0.1%
8.999993385 × 10-83
< 0.1%
1.099999192 × 10-71
 
< 0.1%
1.299999045 × 10-72
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.5030089803 1
< 0.1%
0.5000239425 1
< 0.1%
0.2168271656 1
< 0.1%
0.09932668179 1
< 0.1%
0.09909967926 1
< 0.1%
0.09809110455 1
< 0.1%
0.09480244057 1
< 0.1%
0.0602709757 1
< 0.1%
0.05276847122 1
< 0.1%

harvest
Real number (ℝ)

High correlation  Skewed 

Distinct10224
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0009309795
Minimum2 × 10-9
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.9 KiB
2024-10-20T14:38:32.188682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2 × 10-9
5-th percentile3.3704475 × 10-6
Q19.583805 × 10-6
median1.9383735 × 10-5
Q34.5272735 × 10-5
95-th percentile0.00022718567
Maximum1
Range1
Interquartile range (IQR)3.568893 × 10-5

Descriptive statistics

Standard deviation0.029495809
Coefficient of variation (CV)31.682555
Kurtosis1143.8973
Mean0.0009309795
Median Absolute Deviation (MAD)1.2489865 × 10-5
Skewness33.847086
Sum9.6226041
Variance0.00087000276
MonotonicityNot monotonic
2024-10-20T14:38:32.254700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 9
 
0.1%
1.100000001 × 10-87
 
0.1%
5.800000006 × 10-83
 
< 0.1%
1.870000002 × 10-52
 
< 0.1%
9.82800001 × 10-62
 
< 0.1%
7.299000007 × 10-62
 
< 0.1%
3.953000004 × 10-62
 
< 0.1%
7.583000008 × 10-62
 
< 0.1%
1.168000001 × 10-52
 
< 0.1%
9.319000009 × 10-62
 
< 0.1%
Other values (10214) 10303
99.7%
ValueCountFrequency (%)
2.000000002 × 10-91
 
< 0.1%
2.700000003 × 10-91
 
< 0.1%
4.000000004 × 10-91
 
< 0.1%
5.200000005 × 10-91
 
< 0.1%
6.000000006 × 10-91
 
< 0.1%
6.300000006 × 10-91
 
< 0.1%
8.000000008 × 10-91
 
< 0.1%
9.200000009 × 10-91
 
< 0.1%
1.100000001 × 10-87
0.1%
1.300000001 × 10-81
 
< 0.1%
ValueCountFrequency (%)
1 9
0.1%
0.005371833025 1
 
< 0.1%
0.004698979005 1
 
< 0.1%
0.004648010005 1
 
< 0.1%
0.004558148005 1
 
< 0.1%
0.004424912274 1
 
< 0.1%
0.004395813594 1
 
< 0.1%
0.004327974724 1
 
< 0.1%
0.004149790714 1
 
< 0.1%
0.004049226544 1
 
< 0.1%

agrprod
Real number (ℝ)

High correlation 

Distinct10299
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.074168241
Minimum0
Maximum1
Zeros22
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size80.9 KiB
2024-10-20T14:38:32.319213image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0031290989
Q10.013067215
median0.043200585
Q30.096628591
95-th percentile0.26125515
Maximum1
Range1
Interquartile range (IQR)0.083561376

Descriptive statistics

Standard deviation0.091170156
Coefficient of variation (CV)1.2292344
Kurtosis9.2170839
Mean0.074168241
Median Absolute Deviation (MAD)0.034269359
Skewness2.5642405
Sum766.60294
Variance0.0083119974
MonotonicityNot monotonic
2024-10-20T14:38:32.384966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 22
 
0.2%
0.00693600866 2
 
< 0.1%
0.008209897161 2
 
< 0.1%
0.01219317371 2
 
< 0.1%
0.05285358444 2
 
< 0.1%
0.007595437925 2
 
< 0.1%
0.06066248948 2
 
< 0.1%
0.003675484802 2
 
< 0.1%
0.01056514623 2
 
< 0.1%
0.006558316628 2
 
< 0.1%
Other values (10289) 10296
99.6%
ValueCountFrequency (%)
0 22
0.2%
5.340190963 × 10-61
 
< 0.1%
2.757224913 × 10-51
 
< 0.1%
2.984885686 × 10-51
 
< 0.1%
3.226599592 × 10-51
 
< 0.1%
3.240652726 × 10-51
 
< 0.1%
4.072598266 × 10-51
 
< 0.1%
4.991673237 × 10-51
 
< 0.1%
7.054673325 × 10-51
 
< 0.1%
0.0001459277446 1
 
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.8115547792 1
< 0.1%
0.8022619475 1
< 0.1%
0.7700690279 1
< 0.1%
0.73890966 1
< 0.1%
0.7340847413 1
< 0.1%
0.7286425527 1
< 0.1%
0.6960244695 1
< 0.1%
0.6863044507 1
< 0.1%
0.6825991451 1
< 0.1%

funds
Real number (ℝ)

High correlation 

Distinct9713
Distinct (%)94.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0088004124
Minimum4.3244813 × 10-5
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.9 KiB
2024-10-20T14:38:32.451767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4.3244813 × 10-5
5-th percentile0.0004511257
Q10.0011152886
median0.0022432037
Q30.005182318
95-th percentile0.029829925
Maximum1
Range0.99995676
Interquartile range (IQR)0.0040670295

Descriptive statistics

Standard deviation0.032660275
Coefficient of variation (CV)3.711221
Kurtosis212.50376
Mean0.0088004124
Median Absolute Deviation (MAD)0.0014052951
Skewness11.965117
Sum90.961063
Variance0.0010666936
MonotonicityNot monotonic
2024-10-20T14:38:32.517980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0008008358512 4
 
< 0.1%
0.00129121267 3
 
< 0.1%
0.0005900173867 3
 
< 0.1%
0.0009433178292 3
 
< 0.1%
0.0002522076235 3
 
< 0.1%
0.00395924015 3
 
< 0.1%
0.0008778051644 3
 
< 0.1%
0.001124445825 3
 
< 0.1%
0.0008201185198 3
 
< 0.1%
0.001034728974 3
 
< 0.1%
Other values (9703) 10305
99.7%
ValueCountFrequency (%)
4.324481324 × 10-51
< 0.1%
4.364821635 × 10-51
< 0.1%
4.889245677 × 10-51
< 0.1%
5.752528329 × 10-51
< 0.1%
8.068062172 × 10-51
< 0.1%
9.633266234 × 10-51
< 0.1%
9.738151042 × 10-51
< 0.1%
0.0001020609865 1
< 0.1%
0.0001035939183 1
< 0.1%
0.0001146471635 1
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.7700236798 1
< 0.1%
0.7287283524 1
< 0.1%
0.6479309051 1
< 0.1%
0.5607280619 1
< 0.1%
0.5256051248 1
< 0.1%
0.4990460323 1
< 0.1%
0.4647101347 1
< 0.1%
0.4536418829 1
< 0.1%
0.4461912695 1
< 0.1%

hospitals
Real number (ℝ)

High correlation 

Distinct293
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.014743149
Minimum0
Maximum1
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size80.9 KiB
2024-10-20T14:38:32.582198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0013221684
Q10.0066108418
median0.010577347
Q30.015425297
95-th percentile0.029528427
Maximum1
Range1
Interquartile range (IQR)0.0088144557

Descriptive statistics

Standard deviation0.029592076
Coefficient of variation (CV)2.0071747
Kurtosis340.532
Mean0.014743149
Median Absolute Deviation (MAD)0.0044072279
Skewness15.094315
Sum152.38519
Variance0.00087569095
MonotonicityNot monotonic
2024-10-20T14:38:32.646212image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.007933010137 384
 
3.7%
0.007051564566 361
 
3.5%
0.0004407227854 340
 
3.3%
0.008373732922 339
 
3.3%
0.009695901278 320
 
3.1%
0.006610841781 320
 
3.1%
0.01057734685 316
 
3.1%
0.009255178493 302
 
2.9%
0.01101806963 294
 
2.8%
0.007492287351 283
 
2.7%
Other values (283) 7077
68.5%
ValueCountFrequency (%)
0 3
 
< 0.1%
0.0004407227854 340
3.3%
0.0008814455707 142
1.4%
0.001322168356 90
 
0.9%
0.001762891141 80
 
0.8%
0.002203613927 100
 
1.0%
0.002644336712 143
1.4%
0.003085059498 116
 
1.1%
0.003525782283 141
1.4%
0.003966505068 126
 
1.2%
ValueCountFrequency (%)
1 1
< 0.1%
0.8594094315 1
< 0.1%
0.7981489643 1
< 0.1%
0.757602468 1
< 0.1%
0.5676509476 1
< 0.1%
0.5288673424 1
< 0.1%
0.4909651829 1
< 0.1%
0.4676068753 1
< 0.1%
0.445570736 1
< 0.1%
0.4438078449 1
< 0.1%

beforeschool
Real number (ℝ)

High correlation 

Distinct4273
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.035030893
Minimum0.00033492609
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.9 KiB
2024-10-20T14:38:32.710495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.00033492609
5-th percentile0.0042423972
Q10.0089425267
median0.015428929
Q30.029554437
95-th percentile0.11942348
Maximum1
Range0.99966507
Interquartile range (IQR)0.02061191

Descriptive statistics

Standard deviation0.074923224
Coefficient of variation (CV)2.1387757
Kurtosis48.236569
Mean0.035030893
Median Absolute Deviation (MAD)0.0082782566
Skewness6.2489096
Sum362.07931
Variance0.0056134895
MonotonicityNot monotonic
2024-10-20T14:38:32.776340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01027106685 19
 
0.2%
0.009132318135 19
 
0.2%
0.005247175457 18
 
0.2%
0.009991961774 16
 
0.2%
0.008272674496 15
 
0.1%
0.008205689278 14
 
0.1%
0.00716741839 14
 
0.1%
0.0133412227 14
 
0.1%
0.006296610548 14
 
0.1%
0.01099674005 14
 
0.1%
Other values (4263) 10179
98.5%
ValueCountFrequency (%)
0.000334926093 1
 
< 0.1%
0.001071763498 1
 
< 0.1%
0.001395525387 1
 
< 0.1%
0.001417853794 1
 
< 0.1%
0.001429017997 2
 
< 0.1%
0.001451346403 4
< 0.1%
0.001473674809 1
 
< 0.1%
0.001518331621 6
0.1%
0.001562988434 1
 
< 0.1%
0.001618809449 1
 
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.9412986201 1
< 0.1%
0.931730898 1
< 0.1%
0.9082972357 1
< 0.1%
0.8703501094 1
< 0.1%
0.858125307 1
< 0.1%
0.8251127585 1
< 0.1%
0.823326486 1
< 0.1%
0.8231590229 1
< 0.1%
0.8143504667 1
< 0.1%

factoriescap
Real number (ℝ)

High correlation 

Distinct10335
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.017058953
Minimum4.6530838 × 10-6
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.9 KiB
2024-10-20T14:38:32.841021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4.6530838 × 10-6
5-th percentile0.00010850331
Q10.00050790219
median0.0020310077
Q30.0083208499
95-th percentile0.080539692
Maximum1
Range0.99999535
Interquartile range (IQR)0.0078129477

Descriptive statistics

Standard deviation0.05657858
Coefficient of variation (CV)3.3166503
Kurtosis58.110864
Mean0.017058953
Median Absolute Deviation (MAD)0.0018189456
Skewness6.7148929
Sum176.32133
Variance0.0032011357
MonotonicityNot monotonic
2024-10-20T14:38:32.906078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.001003097723 2
 
< 0.1%
0.001736831258 1
 
< 0.1%
0.002515745611 1
 
< 0.1%
0.001355319514 1
 
< 0.1%
0.002013589377 1
 
< 0.1%
0.001207435059 1
 
< 0.1%
0.001119877193 1
 
< 0.1%
0.001078995446 1
 
< 0.1%
0.00119249939 1
 
< 0.1%
0.001340733224 1
 
< 0.1%
Other values (10325) 10325
99.9%
ValueCountFrequency (%)
4.653083763 × 10-61
< 0.1%
7.305677459 × 10-61
< 0.1%
7.429210656 × 10-61
< 0.1%
7.630096908 × 10-61
< 0.1%
8.26198027 × 10-61
< 0.1%
8.350601911 × 10-61
< 0.1%
8.924194033 × 10-61
< 0.1%
9.584090891 × 10-61
< 0.1%
1.095696281 × 10-51
< 0.1%
1.476579774 × 10-51
< 0.1%
ValueCountFrequency (%)
1 1
< 0.1%
0.8096382239 1
< 0.1%
0.7834796808 1
< 0.1%
0.7274662217 1
< 0.1%
0.7168002017 1
< 0.1%
0.7156079668 1
< 0.1%
0.6675728755 1
< 0.1%
0.663238924 1
< 0.1%
0.6585320954 1
< 0.1%
0.6147017387 1
< 0.1%

Interactions

2024-10-20T14:38:29.146279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:13.122362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:14.225056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:15.087711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:15.957282image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:16.877718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:17.722627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:18.586810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:19.508654image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:20.358309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:21.228266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:22.183944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:23.033551image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:23.890130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:24.737340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:25.659026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:26.504377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:27.366932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:28.288880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:29.192908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:13.214261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:14.271434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:15.134385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:16.002257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:16.924552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:17.782361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:18.631993image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:19.554362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:20.404234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:21.273607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:22.232004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:23.080283image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:23.939662image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:24.783587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:25.705024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:26.549669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:27.411960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:28.335814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:29.236188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:13.297526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:14.314838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:15.178913image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:16.047166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:16.968514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:17.827892image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:18.677071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:19.598873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:20.448617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:21.318479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:22.276012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:23.124812image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:23.982599image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:24.827527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:25.750919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:26.594198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:27.458348image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:28.380191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:29.280468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:13.373273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:14.359500image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:15.222533image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:16.091745image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:17.013414image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:17.872066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:18.721459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:19.644108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:20.492818image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:21.362816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:22.321383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:23.169394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:24.028937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:24.950449image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:25.794383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:26.638226image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:27.502861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:28.424265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:29.325727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:13.432657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:14.403876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:15.268288image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:16.136661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:17.057187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:17.917827image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:18.766918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:19.688054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:20.537435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:21.407987image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:22.364741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:23.214481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:24.073514image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:24.994832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:25.839805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:26.683054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:27.545962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:28.474466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:29.369167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:13.479252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:14.448916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:15.312786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:16.179836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:17.101756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:17.961919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:18.810233image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:19.733621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:20.582270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:21.451844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:22.408521image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:23.259198image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:24.118914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:25.039510image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:25.883550image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:26.726554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:27.668318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:28.521471image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:29.414832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:13.528318image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:14.493089image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:15.357097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:16.224817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:17.145461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:18.005312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:18.855358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:19.778434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:20.625859image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:21.496329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:22.453701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:23.302916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:24.162106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:25.082366image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:25.926954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:26.770512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:27.711997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:28.567596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:29.459139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:13.576557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:14.538074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:15.402901image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:16.268794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:17.190565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:18.050467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:18.899306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:19.821665image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:20.671418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:21.542766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:22.497797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:23.347029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:24.206416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:25.126974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:25.972453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-10-20T14:38:18.542074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:19.464969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:20.312803image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:21.182203image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:22.061833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:22.988183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:23.835138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:24.693419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:25.614215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:26.459339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:27.322351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:28.245208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-20T14:38:29.102125image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-20T14:38:32.958511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
agrprodavgemployersavgsalarybeforeschoolconsnewareasfactoriescapfoodseatsfundsharvesthospitalslivarealivestockpopsizeretailturnoverroadslensaldoservicesnumshopareasportsvenue
agrprod1.0000.039-0.1360.0490.2230.0730.0610.1030.6140.4490.0270.7420.1640.0320.3900.0470.0420.0220.269
avgemployers0.0391.0000.4710.9210.7060.8640.8110.7080.4040.359-0.332-0.0610.9250.7560.2160.0220.8520.8890.738
avgsalary-0.1360.4711.0000.4230.3020.5830.3680.492-0.0250.040-0.015-0.2640.3460.501-0.0470.1400.3530.4010.263
beforeschool0.0490.9210.4231.0000.7190.7590.8210.7220.4350.399-0.369-0.0320.9480.7470.238-0.0120.8550.8940.772
consnewareas0.2230.7060.3020.7191.0000.6350.6980.6350.5640.445-0.1120.1120.7650.6430.3380.1640.6570.6770.690
factoriescap0.0730.8640.5830.7590.6351.0000.6780.6240.3450.262-0.196-0.0720.7550.6900.1770.0780.7020.7350.622
foodseats0.0610.8110.3680.8210.6980.6781.0000.6430.4230.359-0.237-0.0290.8450.7290.2300.0570.8040.8220.698
funds0.1030.7080.4920.7220.6350.6240.6431.0000.3620.365-0.245-0.0070.6990.5770.1310.0690.6290.6480.626
harvest0.6140.404-0.0250.4350.5640.3450.4230.3621.0000.500-0.0930.4150.5550.3620.4370.0680.3920.4010.534
hospitals0.4490.3590.0400.3990.4450.2620.3590.3650.5001.000-0.0260.3550.4610.3030.491-0.0460.3220.3540.540
livarea0.027-0.332-0.015-0.369-0.112-0.196-0.237-0.245-0.093-0.0261.000-0.037-0.357-0.0950.0860.089-0.322-0.282-0.198
livestock0.742-0.061-0.264-0.0320.112-0.072-0.029-0.0070.4150.355-0.0371.0000.052-0.1110.313-0.034-0.046-0.0720.163
popsize0.1640.9250.3460.9480.7650.7550.8450.6990.5550.461-0.3570.0521.0000.7650.3030.0220.8750.9090.816
retailturnover0.0320.7560.5010.7470.6430.6900.7290.5770.3620.303-0.095-0.1110.7651.0000.2140.1160.7340.8080.651
roadslen0.3900.216-0.0470.2380.3380.1770.2300.1310.4370.4910.0860.3130.3030.2141.0000.0000.1930.2410.310
saldo0.0470.0220.140-0.0120.1640.0780.0570.0690.068-0.0460.089-0.0340.0220.1160.0001.0000.0260.031-0.017
servicesnum0.0420.8520.3530.8550.6570.7020.8040.6290.3920.322-0.322-0.0460.8750.7340.1930.0261.0000.8990.728
shoparea0.0220.8890.4010.8940.6770.7350.8220.6480.4010.354-0.282-0.0720.9090.8080.2410.0310.8991.0000.732
sportsvenue0.2690.7380.2630.7720.6900.6220.6980.6260.5340.540-0.1980.1630.8160.6510.310-0.0170.7280.7321.000

Missing values

2024-10-20T14:38:30.038090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-20T14:38:30.240511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

saldopopsizeavgemployersavgsalaryshopareafoodseatsretailturnoverconsnewareaslivareasportsvenueservicesnumroadslenlivestockharvestagrprodfundshospitalsbeforeschoolfactoriescap
0-0.0013340.0159620.0081290.0319170.0064360.0198410.0014690.0037990.0016580.0331130.0109480.2498260.0004020.0000360.0431740.0014910.0092550.0133080.000388
1-0.0003520.0160990.0073940.0328650.0071450.0213750.0014200.0038670.0016900.0326400.0121480.2498260.0003870.0000360.0499820.0007540.0092550.0134980.000411
2-0.0010570.0160490.0073190.0349060.0071670.0222980.0017100.0038190.0017220.0262540.0110980.2498260.0003960.0000420.0549190.0019370.0088140.0125370.000454
30.0017620.0160070.0071490.0367210.0074500.0200680.0019960.0021780.0017540.0262540.0121480.1853380.0004040.0000370.0498450.0012270.0083740.0131400.000446
40.0038270.0159240.0064790.0428840.0065570.0254080.0019310.0042880.0017860.0267270.0095980.1947370.0003790.0000350.0513280.0013080.0083740.0123920.000568
50.0098190.0159070.0061880.0467040.0068390.0254370.0026760.0046010.0018180.0167930.0082480.1947370.0003740.0000370.0556720.0014560.0083740.0123920.000402
60.0082080.0159560.0063180.0514080.0070010.0251240.0031420.0035150.0018340.0167930.0076480.1947370.0003690.0000330.0617760.0016420.0083740.0121020.000410
70.0063190.0160880.0061310.0561490.0072190.0149410.0035120.0040780.0018580.0167930.0076480.1954340.0003700.0000460.0770860.0021840.0074920.0121470.000463
8-0.0039280.0061180.0031690.0311020.0025890.0009370.0002480.0003510.0021050.0134820.0082480.1170300.0002420.0000080.0233210.0002380.0057290.0042420.000146
9-0.0027440.0059660.0029900.0325250.0023480.0009370.0002980.0002920.0021530.0111160.0082480.1170300.0002230.0000060.0411330.0001760.0048480.0042420.000097
saldopopsizeavgemployersavgsalaryshopareafoodseatsretailturnoverconsnewareaslivareasportsvenueservicesnumroadslenlivestockharvestagrprodfundshospitalsbeforeschoolfactoriescap
103260.1100480.1972130.1885220.1205800.1062440.2812240.0271300.1427290.0014670.0423370.0832330.1461360.0018980.0000660.0593550.0474170.0334950.1757130.079453
103270.0385210.2020710.1833550.1441420.1602750.2997440.0578430.1315490.0015480.0402080.0781340.1461360.0018600.0000760.0707820.0833660.0334950.1733580.077804
103280.1380450.2063970.1863520.1522480.1605050.3284620.0621550.1337730.0016900.0409180.0815840.1461360.0001070.0000730.0805620.3458590.0321730.1884410.076735
10329-0.0003520.0156940.0184070.0910040.0052740.0117030.0002220.0000630.0017780.0203410.0086980.1359790.0000170.0000100.0111390.0011040.0083740.0162220.008391
10330-0.0029460.0153440.0184490.0993550.0051260.0119870.0001720.0002180.0018100.0203410.0088480.1107840.0000160.0000060.0094690.0012630.0083740.0156860.010710
10331-0.0123110.0458650.0463370.0680920.0623960.0580030.0137490.0102970.0017460.0286190.0512900.0467510.0001270.0000080.0071730.0028460.0202730.0526950.004141
10332-0.0107250.0458650.0439860.0705970.0640250.0562700.0156920.0167300.0017940.0243610.0518900.0467510.0001170.0000090.0090110.0171490.0189510.0526950.004071
10333-0.0290790.0452890.0437040.0836460.0615360.0558440.0161790.0054710.0018420.0243610.0574390.0467510.0000060.0000130.0101100.0067030.0202730.0549280.004516
10334-0.0242450.0449850.0439600.0896700.0554640.0414710.0186210.0028330.0018980.0250710.0556390.0467510.0000070.0000080.0073070.0065890.0202730.0547490.004984
10335-0.0234650.0441940.0441360.0965880.0615640.0414710.0284620.0026710.0019850.0253070.0545890.0462060.0000050.0000120.0112470.0063040.0207140.0550510.005150